Transcript SKA AAVP Antenna Array developments at University of Cambridge

Analysis of Low Frequency
Phased Array Stations
Dr. Nima Razavi-Ghods
Dr. Eloy de Lera Acedo
Cambridge AAVP 2010, 09/12/10
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Phased array design parameters
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AA-lo station configuration studies
(regular vs. random)
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Randomisation of elements
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Simulations to compute TA and A/T
(geometries, weighting, element types)
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Future work and conclusions
Overview
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Array size (fundamental limit on Aeff/Tsys)
 Array geometry (main and side-lobe profile)
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◦ Fully filled grids (regular lattice)
◦ Sparse or thinned grids
◦ Truly randomised grids
Antenna element response (scan/polarisation response, matching,
mutual coupling)
 Operating frequency, processing bandwidth, integration time
 Weighting schemes (main beam and side-lobe profile)
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◦ Spatial windows (e.g. Hamming, Gaussian, Kaiser)
◦ Side-lobe profile control (e.g. Dolph-Chebyshev/Taylor, Fourier
design method)
◦ Adaptive nulling
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Back-end processing
◦ Fully digital core (any weighting in single or multiple stages)
◦ First level analogue (some limitations in response)
Factors Affecting Beam on the Sky
Antenna Array Geometries
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Sky (Haslam) Lat = 28.59S, Long =
115.45E Date: 01/01/2020, Time 19.33h
Triangular Lattice Beam
10,000 elements, d = 0.8
Random Vs. Regular
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Sky (Haslam) Lat = 28.59S, Long =
115.45E Date: 01/01/2020, Time 19.33h
Random Lattice Beam
10,000 elements
Random Vs. Regular
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d = /3 : 2
Randomised Array: AA-lo
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mean = 1.45, std = 0.10
1800
Variable min. distance
1600
mean = 1.45, std = 0.22
1400
700
1200
1000
500
800
Frequency
Frequency
600
600
400
400
300
200
200
0
1.4
1.6
1.8
2
2.2
2.4
dmin (min)
Fixed min. distance
2.6
100
0
0.8
1
1.2
1.4
1.6
1.8
2
2.2
dmin (min)
Randomisation algorithm
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TA was analysed as the beam
tracked 3 cold patches on the
sky over four and half hours.
Array factor based simulations
carried computed using NFFT.
AA-lo Station ~10k elements.
6 Geometries: regular,
triangular, sparse random,
thinned, concentric rings, and
fully random.
4 minimum inter-element
separations: 0.5, 0.8, 1, 2.
3 Weights: Uniform, Taylor and
Dolph-Chebyshev (SLL = 35 dB)
3 Element types.
Simulations to compute TA
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Region 1: 09h07m12s 0000’46’’, Region 2: 04h03m36s
-3448’00’’ Region 3: 04h45m00s -6100’00’’
R1
R2
R3
SKA AA-lo observable Sky
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Results for TA: Region 1
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Results for TA: Region 2
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Results for A/T: Region 1
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Results for A/T: Region 2
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Taylor Weighting (SLL = 35 dB)
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AA-lo Observable Sky
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Regular Array (d = 0.5): Sensitivity @ 100 MHzRegular Array (d = 0.8): Sensitivity @ 100 MHzRegular Array (d = 1.0): Sensitivity @ 100 MHz
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100
140
90
120
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33.5
Aeff/Tsys (m2/K)
34.5
Aeff/Tsys (m2/K)
Aeff/Tsys (m2/K)
2
80
70
60
50
33
40
60
40
80
Local Sidereal Time /degrees (R2)
Cosine
Cosine
Bow-tie
100
80
60
40
40
60
20
80
Local Sidereal Time /degrees (R2)
40
60
80
Local Sidereal Time /degrees (R2)
100
100
48
90
90
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80
80
46
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44
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Aeff/Tsys (m2/K)
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Aeff/Tsys (m2/K)
Aeff/Tsys (m2/K)
Random Array (d = 0.5): Sensitivity @ 100 MHzRandom Array (d = 0.8): Sensitivity @ 100 MHzRandom Array (d = 1.0): Sensitivity @ 100 MHz
70
60
50
40
40
60
80
Local Sidereal Time /degrees (R2)
30
70
60
50
40
40
60
80
Local Sidereal Time /degrees (R2)
30
40
60
80
Local Sidereal Time /degrees (R2)
Low Gain vs. High Gain Element
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Xarray Tool: MATLAB GUI
www.mrao.cam.ac.uk/~nima/x
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Main objective: SKA simulator
Faster and more accurate simulations of
the station beam based on MBF approach
(collaboration with UCL).
Computation framework for station
simulator (collaboration with Oxford).
Further analysis of beam synthesis
techniques and weight calibration.
Design of optimal geometry, e.g. far out
versus close in side-lobes.
Future work and collaborations
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Thank You.
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